A common theme in most NCD methodologies is to learn a feature extractor using the labeled data and use clustering [1]}, [2]}, [3]}, psuedo-labelling based learning [4]}, [5]} or contrastive learning [6]}, [7]} to identify classes in the unlabeled pool.
In contrast, we propose a novel Spacing Loss which ensures separability in the latent space of feature extractor, for the labeled and unlabeled classes.
This is achieved by transporting semantically dissimilar instances to equidistant areas in the latent space, identified via multi-dimensional scaling [8]}.
We note that our proposed loss formulation is orthogonal to the existing methodologies, and can easily complement these methods. Our experimental evaluation on CIFAR-10 [9]} and CIFAR-100 [9]} datasets suggests that the models trained with the proposed Spacing Loss achieve state-of-the-art performance when compared to two-stage NCD methods. Further, when combined with single-stage methodologies,
our loss formulation improves each of them consistently.
